




"""simple docstring"""

import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


a							      =     pytest.mark.integration





@require_faiss
class 							lowercase_			(	_A				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			str						):
       _A     =  Dataset.from_dict({'filename': ['my_name-train' + '_' + str(snake_case_						) for x in np.arange(30						).tolist()]}						)
       return dset

 def      lowerCAmelCase_				(					self			:			int						):
       import faiss

       _A     =  self._create_dummy_dataset()
       _A     =  dset.map(
           lambda _UpperCAmelCase       ,				_UpperCAmelCase						: {"vecs": i * np.ones(5       ,				dtype=np.floataa						)}       ,				with_indices=snake_case_       ,				keep_in_memory=snake_case_						)
       _A     =  dset.add_faiss_index('vecs'       ,				batch_size=100       ,				metric_type=faiss.METRIC_INNER_PRODUCT						)
       _A     =  dset.get_nearest_examples('vecs'       ,				np.ones(5       ,				dtype=np.floataa						)						)
       self.assertEqual(examples['filename'][0]       ,				'my_name-train_29'						)
       dset.drop_index('vecs'						)

 def      lowerCAmelCase_				(					self			:			Any						):
       import faiss

       _A     =  self._create_dummy_dataset()
       dset.add_faiss_index_from_external_arrays(
           external_arrays=np.ones((30, 5)						) * np.arange(30						).reshape(-1       ,				1						)       ,				index_name='vecs'       ,				batch_size=100       ,				metric_type=faiss.METRIC_INNER_PRODUCT       ,				)
       _A     =  dset.get_nearest_examples('vecs'       ,				np.ones(5       ,				dtype=np.floataa						)						)
       self.assertEqual(examples['filename'][0]       ,				'my_name-train_29'						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       import faiss

       _A     =  self._create_dummy_dataset()
       dset.add_faiss_index_from_external_arrays(
           external_arrays=np.ones((30, 5)						) * np.arange(30						).reshape(-1       ,				1						)       ,				index_name='vecs'       ,				metric_type=faiss.METRIC_INNER_PRODUCT       ,				)

       # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
       # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
       # see https://bugs.python.org/issue14243 and
       # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
       with tempfile.NamedTemporaryFile(delete=snake_case_						) as tmp_file:
             dset.save_faiss_index('vecs'       ,				tmp_file.name						)
             dset.load_faiss_index('vecs2'       ,				tmp_file.name						)
       os.unlink(tmp_file.name						)

       _A     =  dset.get_nearest_examples('vecs2'       ,				np.ones(5       ,				dtype=np.floataa						)						)
       self.assertEqual(examples['filename'][0]       ,				'my_name-train_29'						)

 def      lowerCAmelCase_				(					self			:			List[str]						):
       _A     =  self._create_dummy_dataset()
       dset.add_faiss_index_from_external_arrays(
           external_arrays=np.ones((30, 5)						) * np.arange(30						).reshape(-1       ,				1						)       ,				index_name='vecs'						)
       dset.drop_index('vecs'						)
       self.assertRaises(snake_case_       ,				partial(dset.get_nearest_examples       ,				'vecs2'       ,				np.ones(5       ,				dtype=np.floataa						)						)						)



 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       from elasticsearch import Elasticsearch

       _A     =  self._create_dummy_dataset()
       with patch('elasticsearch.Elasticsearch.search'						) as mocked_search, patch(
           'elasticsearch.client.IndicesClient.create'						) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'						) as mocked_bulk:
             _A     =  {"""acknowledged""": True}
             mocked_bulk.return_value([(True, None)] * 30						)
             _A     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
             _A     =  Elasticsearch()

             dset.add_elasticsearch_index('filename'       ,				es_client=snake_case_						)
             _A     =  dset.get_nearest_examples('filename'       ,				'my_name-train_29'						)
             self.assertEqual(examples['filename'][0]       ,				'my_name-train_29'						)





@require_faiss
class 							lowercase_			(	_A				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			List[str]						):
       import faiss

       _A     =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT						)

       # add vectors
       index.add_vectors(np.eye(5       ,				dtype=np.floataa						)						)
       self.assertIsNotNone(index.faiss_index						)
       self.assertEqual(index.faiss_index.ntotal       ,				5						)
       index.add_vectors(np.zeros((5, 5)       ,				dtype=np.floataa						)						)
       self.assertEqual(index.faiss_index.ntotal       ,				10						)

       # single query
       _A     =  np.zeros(5       ,				dtype=np.floataa						)
       _A     =  1
       _A     =  index.search(snake_case_						)
       self.assertRaises(snake_case_       ,				index.search       ,				query.reshape(-1       ,				1						)						)
       self.assertGreater(scores[0]       ,				0						)
       self.assertEqual(indices[0]       ,				1						)

       # batched queries
       _A     =  np.eye(5       ,				dtype=np.floataa						)[::-1]
       _A     =  index.search_batch(snake_case_						)
       self.assertRaises(snake_case_       ,				index.search_batch       ,				queries[0]						)
       _A     =  [scores[0] for scores in total_scores]
       _A     =  [indices[0] for indices in total_indices]
       self.assertGreater(np.min(snake_case_						)       ,				0						)
       self.assertListEqual([4, 3, 2, 1, 0]       ,				snake_case_						)

 def      lowerCAmelCase_				(					self			:			Union[str, Any]						):
       import faiss

       _A     =  FaissIndex(string_factory='Flat'						)
       index.add_vectors(np.eye(5       ,				dtype=np.floataa						)						)
       self.assertIsInstance(index.faiss_index       ,				faiss.IndexFlat						)
       _A     =  FaissIndex(string_factory='LSH'						)
       index.add_vectors(np.eye(5       ,				dtype=np.floataa						)						)
       self.assertIsInstance(index.faiss_index       ,				faiss.IndexLSH						)
       with self.assertRaises(snake_case_						):
             _A     =  FaissIndex(string_factory='Flat'       ,				custom_index=faiss.IndexFlat(5						)						)

 def      lowerCAmelCase_				(					self			:			Optional[int]						):
       import faiss

       _A     =  faiss.IndexFlat(5						)
       _A     =  FaissIndex(custom_index=snake_case_						)
       index.add_vectors(np.eye(5       ,				dtype=np.floataa						)						)
       self.assertIsInstance(index.faiss_index       ,				faiss.IndexFlat						)



 def      lowerCAmelCase_				(					self			:			int						):
       import faiss

       _A     =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT						)
       index.add_vectors(np.eye(5       ,				dtype=np.floataa						)						)

       # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
       # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
       # see https://bugs.python.org/issue14243 and
       # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
       with tempfile.NamedTemporaryFile(delete=snake_case_						) as tmp_file:
             index.save(tmp_file.name						)
             _A     =  FaissIndex.load(tmp_file.name						)
       os.unlink(tmp_file.name						)

       _A     =  np.zeros(5       ,				dtype=np.floataa						)
       _A     =  1
       _A     =  index.search(snake_case_						)
       self.assertGreater(scores[0]       ,				0						)
       self.assertEqual(indices[0]       ,				1						)






@require_faiss
def 		_snake_case	(   _snake_case				:   int				)       ->   Optional[int]:






      '''simple docstring'''


      import faiss

      _A     =  FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT				)
      index.add_vectors(np.eye(5							,    dtype=np.floataa				)				)

      _A     =  """index.faiss"""
      _A     =  F'''mock://{index_name}'''
      index.save(_snake_case							,    storage_options=mockfs.storage_options				)
      _A     =  FaissIndex.load(_snake_case							,    storage_options=mockfs.storage_options				)

      _A     =  np.zeros(5							,    dtype=np.floataa				)
      _A     =  1
      _A     =  index.search(_snake_case				)
      assert scores[0] > 0
      assert indices[0] == 1







@require_elasticsearch
class 							lowercase_			(	_A				):



 '''simple docstring'''

 def      lowerCAmelCase_				(					self			:			int						):
       from elasticsearch import Elasticsearch

       with patch('elasticsearch.Elasticsearch.search'						) as mocked_search, patch(
           'elasticsearch.client.IndicesClient.create'						) as mocked_index_create, patch('elasticsearch.helpers.streaming_bulk'						) as mocked_bulk:
             _A     =  Elasticsearch()
             _A     =  {"""acknowledged""": True}
             _A     =  ElasticSearchIndex(es_client=snake_case_						)
             mocked_bulk.return_value([(True, None)] * 3						)
             index.add_documents(['foo', 'bar', 'foobar']						)

             # single query
             _A     =  """foo"""
             _A     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
             _A     =  index.search(snake_case_						)
             self.assertEqual(scores[0]       ,				1						)
             self.assertEqual(indices[0]       ,				0						)

             # single query with timeout
             _A     =  """foo"""
             _A     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
             _A     =  index.search(snake_case_       ,				request_timeout=30						)
             self.assertEqual(scores[0]       ,				1						)
             self.assertEqual(indices[0]       ,				0						)

             # batched queries
             _A     =  ["""foo""", """bar""", """foobar"""]
             _A     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
             _A     =  index.search_batch(snake_case_						)
             _A     =  [scores[0] for scores in total_scores]
             _A     =  [indices[0] for indices in total_indices]
             self.assertGreater(np.min(snake_case_						)       ,				0						)
             self.assertListEqual([1, 1, 1]       ,				snake_case_						)

             # batched queries with timeout
             _A     =  ["""foo""", """bar""", """foobar"""]
             _A     =  {"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
             _A     =  index.search_batch(snake_case_       ,				request_timeout=30						)
             _A     =  [scores[0] for scores in total_scores]
             _A     =  [indices[0] for indices in total_indices]
             self.assertGreater(np.min(snake_case_						)       ,				0						)
             self.assertListEqual([1, 1, 1]       ,				snake_case_						)



import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
							lowerCamelCase_    :			int    =	argparse.ArgumentParser()

							parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")

							parser.add_argument(
							    """--txt2img_unclip""",
							    default="""kakaobrain/karlo-v1-alpha""",
							    type=str,
							    required=False,
							    help="""The pretrained txt2img unclip.""",
							)

							lowerCamelCase_    :			Dict    =	parser.parse_args()

							lowerCamelCase_    :			str    =	UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

							lowerCamelCase_    :			int    =	CLIPImageProcessor()
							lowerCamelCase_    :			Any    =	CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")

							lowerCamelCase_    :			Union[str, Any]    =	UnCLIPImageVariationPipeline(
							    decoder=txtaimg.decoder,
							    text_encoder=txtaimg.text_encoder,
							    tokenizer=txtaimg.tokenizer,
							    text_proj=txtaimg.text_proj,
							    feature_extractor=feature_extractor,
							    image_encoder=image_encoder,
							    super_res_first=txtaimg.super_res_first,
							    super_res_last=txtaimg.super_res_last,
							    decoder_scheduler=txtaimg.decoder_scheduler,
							    super_res_scheduler=txtaimg.super_res_scheduler,
							)

							imgaimg.save_pretrained(args.dump_path)






